Evaluating influential spreaders in networks is of great significance for promoting the dissemination of beneficial information or inhibiting the spreading of harmful information. Currently, there are some central indices that can be used to evaluate spreading influence of {nodes}. However, most of them ignore the spreading probability and take into consideration only the network topology or the location of source node, so the excellent results can be achieved only when the spreading probability is in a specified range. For example, the degree centrality is appropriate for a minor spreading probability, but to ensure the accuracy, semi-local and closeness centralities are more suitable for a slightly larger one. To solve the sensitivity problem of spreading probability, a novel algorithm is proposed based on the extension of degree. In this algorithm, the coverage area of degree is recursively extended by the overlapping of degree of neighbors, which makes different extension levels correspond to different spreading probabilities. For a certain spreading probability, the proper level index is calculated by finding the most correlate ranking sequences of sampling {nodes}, which is obtained by matching the results of different spreading levels and SIR simulation. In this paper, the relationship between extension level and spreading probability is explained by the theory of fitting the weight and infected possibility of {nodes}, and the feasibility of the sampling method is verified by the computational experiments. The experimental results on both real and computer-generated datasets show that the proposed algorithm can effectively evaluate the spreading influences of {nodes} under different spreading probabilities, and the performance is close or even superior to that evaluated by using other central indices.